The transition of power grids from systems based on synchronous generators to those integrating increasing shares of renewable energy sources (RES) reduces system inertia and challenges frequency stability, particularly in weak grids. Grid-forming (GFM) control has emerged as a key solution, enabling inverter-based resources to provide synthetic inertia and autonomous grid support. This paper proposes a method for assigning virtual inertia and damping coefficients to multiple GFM inverters in a microgrid or distribution feeder. The objective is to ensure that the aggregated dynamic response at the point of common coupling aligns with target values specified by the grid operator. The proposed method is validated through simulations on an IEEE test network.

This paper presents a black-box method for generating surrogate data from measurements taken on inductors working up to magnetic saturation. The proposed method is based on a sequential neural network architecture, optimized through the Python Tensorflow framework and the Keras API, which accurately predicts the inductor flux dynamics under varying operating conditions. The generated data can be useful to fit existing circuit models to a limited set of physical measurements (easy-to-measure quantities, i.e., inductor voltage and current), complemented by the obtained surrogate data. The proposed black-box model performs well in predicting flux across different frequencies and amplitudes. This research is a first proof of concept (we focus on zero-bias sinusoidal inputs and amorphous-core inductors at fixed temperature); anyway, it highlights the potential of combining deep learning with robust optimization and pre-processing techniques to improve predictive accuracy in circuit models of inductors working up to magnetic saturation. These models can be used for simulating and designing high-power-density switch-mode power supplies.

Digital ΔΣ modulators (DDSM) are used in applications that require a reduction of the wordlength of a digital signal. In the presence of non-idealities, the quantization error of the DDSM can interact with nonlinearities further along the signal chain and generate spurious tones in addition to excess noise. This paper introduces the INIS family of DDSMs that are inherently immune from nonlinearity-induced spurs. Some representative members of the family are analyzed through simulations; one is demonstrated with a hardware implementation. Their performances are compared with those of other well-known DDSM architectures.

In this work, a nonlinear behavioral model is proposed for air-gapped ferrite-core inductors working up to magnetic saturation. The component is represented through the series connection of a nonlinear conservative inductor and a linear resistor, accounting for the instantaneous losses in both the windings and the core. The model is identified and validated through experimental measurements collected on a real buck converter. A very limited set of inductor voltages and currents is used for parameter identification. Some model coefficients depend explicitly on the air-gap length, which is useful for converter design purposes.

In this work, a nonlinear behavioral circuit model is proposed for air-gapped ferrite-core inductors working up to magnetic saturation. The model comprises a nonlinear conservative inductor, with current-dependent inductance, and two linear resistors, accounting for losses in both the winding and the core. Two representations are proposed for the nonlinear inductance, parameterized by both the number of turns and the air-gap length. The model (in both versions) is identified and validated through experimental measurements collected on a real buck converter. Inductor voltages and currents are used for parameter identification. The obtained results exhibit a good match with the experimental measurements used for validation purposes. An example of the application of the model to the design of a buck converter exploiting partially saturating inductors is also proposed.

In this paper, the derivative-free optimization algorithm MADS (mesh adaptive direct search) is adapted for implementation on field programmable gate array (FPGA) with fixed-point data representation. MADS is then exploited to solve constrained nonlinear optimization problems arising from non-linear model predictive control. The application on two examples taken from the literature shows the advantages of the proposed circuit architecture over the existing work, in terms of latency and resource occupation.